Evaluation of the Hematology Analyzer Test Performance Using Six Sigma Metrics: A Cross Sectional Study

Abstract Introduction/Objective The six sigma metric is used to assess the performance of a measurement procedure in comparison to the medical requirement. In clinical laboratories, it can be used to assess the quality of instruments or processes. This study aims to evaluate the performance of hematology analyzers using the six sigma scale. Methods/Case Report This is a descriptive cross-sectional study wherein quality control data from hematology analyzers were obtained. The mean, standard deviation, coefficient of variation, bias and sigma value of the RBC, WBC, hemoglobin, hematocrit, and platelet parameters were determined and the performance of Beckman Coulter DxH 900 was assessed by plotting a normalized OPSpecs chart. Results (if a Case Study enter NA) The WBC and platelet parameters had sigma values of more than 6. Less stringent quality control measures could be applied these parameters which include fewer Westgard rules and fewer control runs. The RBC and hemoglobin parameters had sigma values of at least 3. The hematocrit parameter had a sigma value of less than 3. Improving this parameter could be done by performing optical channel evaluation, using multiple Westgard control rules, increasing the number of control materials and the number of runs. Conclusion This study has demonstrated the analytical performance of hematology analyzers on the six sigma scale, which in turn allows for the selection of the appropriate statistical quality control rule. Ultimately, statistical quality control is only one part of a laboratory’s quality management system. The sigma quality of the testing process, as well as choosing the appropriate control rules and number of controls, may provide the basis for the total quality control strategy of the laboratory. Other pre-analytic and post-analytic factors must also be considered in formulating a laboratory total quality control plan. This would allow the laboratory to provide the best possible quality of services to its clients by increasing efficiency, reducing wastage, and incorporating continuous changes to improve the system.

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Publication Details

Journal
American Journal of Clinical Pathology
Published
2024-10-01
DOI
https://doi.org/10.1093/ajcp/aqae129.162
Citations
1
Primary Topic
Clinical Laboratory Practices and Quality Control
Type
article
Field-Weighted Citation Impact
0.33
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article

Evaluation of the Hematology Analyzer Test Performance Using Six Sigma Metrics: A Cross Sectional Study

Karen Damian, Emilio Q. Villanueva, P R Dy
1 citations
American Journal of Clinical Pathology
Clinical Laboratory Practices and Quality Control
0.33
article

Evaluation of the Hematology Analyzer Test Performance Using Six Sigma Metrics: A Cross Sectional Study

Karen Damian, Emilio Q. Villanueva, P R Dy
article en
1 citations

Abstract

Abstract Introduction/Objective The six sigma metric is used to assess the performance of a measurement procedure in comparison to the medical requirement. In clinical laboratories, it can be used to assess the quality of instruments or processes. This study aims to evaluate the performance of hematology analyzers using the six sigma scale. Methods/Case Report This is a descriptive cross-sectional study wherein quality control data from hematology analyzers were obtained. The mean, standard deviation, coefficient of variation, bias and sigma value of the RBC, WBC, hemoglobin, hematocrit, and platelet parameters were determined and the performance of Beckman Coulter DxH 900 was assessed by plotting a normalized OPSpecs chart. Results (if a Case Study enter NA) The WBC and platelet parameters had sigma values of more than 6. Less stringent quality control measures could be applied these parameters which include fewer Westgard rules and fewer control runs. The RBC and hemoglobin parameters had sigma values of at least 3. The hematocrit parameter had a sigma value of less than 3. Improving this parameter could be done by performing optical channel evaluation, using multiple Westgard control rules, increasing the number of control materials and the number of runs. Conclusion This study has demonstrated the analytical performance of hematology analyzers on the six sigma scale, which in turn allows for the selection of the appropriate statistical quality control rule. Ultimately, statistical quality control is only one part of a laboratory’s quality management system. The sigma quality of the testing process, as well as choosing the appropriate control rules and number of controls, may provide the basis for the total quality control strategy of the laboratory. Other pre-analytic and post-analytic factors must also be considered in formulating a laboratory total quality control plan. This would allow the laboratory to provide the best possible quality of services to its clients by increasing efficiency, reducing wastage, and incorporating continuous changes to improve the system.

American Journal of Clinical PathologyVol. 162(Supplement_1)
Philippine General Hospital (PH), University of the Philippines Manila (PH)
Openalex Percentile: Top 37%
Clinical Laboratory Practices and Quality Control
0.33
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